1. Field of Invention
The current invention relates to automated identification of soft tissue substructures in a soft tissue region of a human or animal subject in a non-invasive manner.
2. Discussion of Related Art
Although advancements in imaging hardware and software have substantially improved imaging throughput and image quality, there remains a bottleneck in radiological diagnosis. Currently, radiological diagnosis is mostly based on subjective visual inspection and judgment. However, manual reading is laborious and time-consuming. Furthermore, quite often, the amount of abnormality is not large compared to the normal range of subject variability. Quantitative analysis may significantly benefit current radiological diagnosis by improving our ability to detect and characterize abnormalities in a robust and reproducible manner. The current lack of quantitative analysis in clinical routines stems from, at least partly, difficulties in analyzing tissues based on magnetic resonance (MR) images. In neurological diagnosis, for example, high quality segmentation of the brain boundary requires a considerable amount of manual labor, which typically takes 2-4 hours for segmenting individual brains. Further segmentation of the brain into tissue classes takes even more time by manual labor. This may hinder delivery of quality service to our aging society with rising incidences of, for example, Alzheimer's disease. Existing automated programs for segmentation only provide approximate segmentation results that are inadequate for wide adoption in clinical routines. Thus, there is a need for an improved automatic segmentation of soft tissue structures of a person or an animal.